Most people who tested Falcon 9 flight systems at SpaceX stay in aerospace. Levi Malott left. Not because he didn't like rockets - he just kept finding harder problems.
Born and raised in Missouri, Malott studied computer science at Missouri University of Science and Technology, where he didn't just take classes - he ran machine learning research on internet usage patterns and mental health, co-authoring papers and doing the actual math behind models that tried to understand depression and anxiety through behavioral data. That was 2013. That mix of engineering rigor and human-scale questions never left him.
SpaceX came next. He started with something real: testing the critical subsystems of Falcon 9 rockets. Not debugging web apps. Not tuning dashboards. Literally testing whether rockets would work. He moved to the crew capsule, running hardware-software integration testing for the systems that would eventually carry humans to the International Space Station. Then he quietly shifted to building internal developer tools for SpaceX's own software teams - the kind of infrastructure work that doesn't make headlines but holds everything else up.
The jump to BetterUp was the first real pivot. He joined as one of the first 80 employees post-Series B, building the ML and data infrastructure that would eventually underpin a platform worth $4.5 billion. It was his first close look at what venture-backed hypergrowth actually looks like from the inside - and what it takes to build data systems that don't fall apart when user counts 10x.
Then Pachama. He joined as employee number seven. That's not a metaphor for "early" - that's literally seven people building a verified forest carbon marketplace. He stayed until the company had around 40 engineers and a Series B valuation of roughly $700 million. His job was VP of Engineering. His actual job was figuring out how to measure trees from space, faster and more accurately than humans can, and turn that measurement into financial instruments people could trust.
That's a different kind of engineering problem. It requires satellite imagery, machine learning, carbon accounting standards, and the ability to explain it all to scientists, regulators, and investors in the same room. Malott was the person connecting those worlds - technically and organizationally.